Overview

Brought to you by YData

Dataset statistics

Number of variables28
Number of observations13178
Missing cells69504
Missing cells (%)18.8%
Total size in memory2.6 MiB
Average record size in memory204.0 B

Variable types

Numeric8
Text20

Alerts

kingdom has constant value "ANIMALIA" Constant
phylum has constant value "CHORDATA" Constant
class has constant value "MAMMALIA" Constant
subspecies has 11850 (89.9%) missing values Missing
subpop has 12930 (98.1%) missing values Missing
source has 12098 (91.8%) missing values Missing
island has 7002 (53.1%) missing values Missing
tax_comm has 13078 (99.2%) missing values Missing
dist_comm has 12546 (95.2%) missing values Missing
SHAPE_Area is highly skewed (γ1 = 20.68505755) Skewed
generalisd has 13114 (99.5%) zeros Zeros

Reproduction

Analysis started2025-08-05 11:59:11.980022
Analysis finished2025-08-05 11:59:12.437885
Duration0.46 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

id_no
Real number (ℝ)

Distinct2968
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12794512.95
Minimum137
Maximum272126582
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size103.1 KiB
2025-08-05T11:59:12.522110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum137
5-th percentile3787
Q112518
median18764
Q341772
95-th percentile88150966
Maximum272126582
Range272126445
Interquartile range (IQR)29254

Descriptive statistics

Standard deviation38954311.49
Coefficient of variation (CV)3.044610736
Kurtosis13.56832363
Mean12794512.95
Median Absolute Deviation (MAD)10978
Skewness3.549552471
Sum1.686060917 × 1011
Variance1.517438384 × 1015
MonotonicityNot monotonic
2025-08-05T11:59:12.655581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12594 314
 
2.4%
10118 148
 
1.1%
18764 148
 
1.1%
22103 118
 
0.9%
29684 112
 
0.8%
12551 102
 
0.8%
18150 98
 
0.7%
136798 84
 
0.6%
9760 80
 
0.6%
7951 74
 
0.6%
Other values (2958) 11900
90.3%
ValueCountFrequency (%)
137 14
0.1%
138 2
 
< 0.1%
142 14
0.1%
266 2
 
< 0.1%
268 2
 
< 0.1%
ValueCountFrequency (%)
272126582 2
< 0.1%
271004455 2
< 0.1%
271004387 2
< 0.1%
271001733 2
< 0.1%
271000210 2
< 0.1%
Distinct2968
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Memory size103.1 KiB
2025-08-05T11:59:12.883532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length33
Median length29
Mean length18.38442859
Min length8

Characters and Unicode

Total characters242270
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMicrocebus boraha
2nd rowCyclopes rufus
3rd rowCyclopes rufus
4th rowHipposideros pygmaeus
5th rowHipposideros pygmaeus
ValueCountFrequency (%)
pteropus 402
 
1.5%
rhinolophus 392
 
1.5%
hipposideros 376
 
1.4%
macroglossus 338
 
1.3%
myotis 332
 
1.3%
minimus 332
 
1.3%
crocidura 288
 
1.1%
dobsonia 272
 
1.0%
nyctimene 178
 
0.7%
pipistrellus 176
 
0.7%
Other values (3262) 23270
88.3%
2025-08-05T11:59:13.219073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 24396
 
10.1%
i 20608
 
8.5%
a 20560
 
8.5%
o 17820
 
7.4%
u 16200
 
6.7%
e 15920
 
6.6%
r 15470
 
6.4%
13178
 
5.4%
n 12440
 
5.1%
l 11186
 
4.6%
Other values (41) 74492
30.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 242270
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 24396
 
10.1%
i 20608
 
8.5%
a 20560
 
8.5%
o 17820
 
7.4%
u 16200
 
6.7%
e 15920
 
6.6%
r 15470
 
6.4%
13178
 
5.4%
n 12440
 
5.1%
l 11186
 
4.6%
Other values (41) 74492
30.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 242270
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 24396
 
10.1%
i 20608
 
8.5%
a 20560
 
8.5%
o 17820
 
7.4%
u 16200
 
6.7%
e 15920
 
6.6%
r 15470
 
6.4%
13178
 
5.4%
n 12440
 
5.1%
l 11186
 
4.6%
Other values (41) 74492
30.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 242270
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 24396
 
10.1%
i 20608
 
8.5%
a 20560
 
8.5%
o 17820
 
7.4%
u 16200
 
6.7%
e 15920
 
6.6%
r 15470
 
6.4%
13178
 
5.4%
n 12440
 
5.1%
l 11186
 
4.6%
Other values (41) 74492
30.7%

presence
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.220063743
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.6 KiB
2025-08-05T11:59:13.305263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8691335457
Coefficient of variation (CV)0.7123673259
Kurtosis16.97779892
Mean1.220063743
Median Absolute Deviation (MAD)0
Skewness4.180419867
Sum16078
Variance0.7553931203
MonotonicityNot monotonic
2025-08-05T11:59:13.382915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 12244
92.9%
3 294
 
2.2%
5 216
 
1.6%
6 180
 
1.4%
4 152
 
1.2%
2 92
 
0.7%
ValueCountFrequency (%)
1 12244
92.9%
2 92
 
0.7%
3 294
 
2.2%
4 152
 
1.2%
5 216
 
1.6%
ValueCountFrequency (%)
6 180
1.4%
5 216
1.6%
4 152
1.2%
3 294
2.2%
2 92
 
0.7%

origin
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.068902717
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.6 KiB
2025-08-05T11:59:13.461355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4146366221
Coefficient of variation (CV)0.3879086615
Kurtosis55.08389056
Mean1.068902717
Median Absolute Deviation (MAD)0
Skewness7.056509191
Sum14086
Variance0.1719235284
MonotonicityNot monotonic
2025-08-05T11:59:13.540788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 12740
96.7%
3 220
 
1.7%
2 130
 
1.0%
5 66
 
0.5%
4 18
 
0.1%
6 4
 
< 0.1%
ValueCountFrequency (%)
1 12740
96.7%
2 130
 
1.0%
3 220
 
1.7%
4 18
 
0.1%
5 66
 
0.5%
ValueCountFrequency (%)
6 4
 
< 0.1%
5 66
 
0.5%
4 18
 
0.1%
3 220
1.7%
2 130
1.0%

seasonal
Real number (ℝ)

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.030808924
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.6 KiB
2025-08-05T11:59:13.614690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum5
Range4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.339802448
Coefficient of variation (CV)0.3296463972
Kurtosis123.5559712
Mean1.030808924
Median Absolute Deviation (MAD)0
Skewness11.14466036
Sum13584
Variance0.1154657036
MonotonicityNot monotonic
2025-08-05T11:59:13.694433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
1 13066
99.2%
5 82
 
0.6%
4 24
 
0.2%
2 6
 
< 0.1%
ValueCountFrequency (%)
1 13066
99.2%
2 6
 
< 0.1%
4 24
 
0.2%
5 82
 
0.6%
ValueCountFrequency (%)
5 82
 
0.6%
4 24
 
0.2%
2 6
 
< 0.1%
1 13066
99.2%
Distinct88
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size103.1 KiB
2025-08-05T11:59:13.902038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length161
Median length4
Mean length8.594475641
Min length4

Characters and Unicode

Total characters113258
Distinct characters74
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIUCN
2nd rowIUCN
3rd rowIUCN
4th rowIUCN
5th rowIUCN
ValueCountFrequency (%)
iucn 12096
55.9%
ssc 1202
 
5.6%
group 1202
 
5.6%
specialist 1180
 
5.4%
mammal 1130
 
5.2%
small 1130
 
5.2%
norway 148
 
0.7%
and 124
 
0.6%
inland 112
 
0.5%
mallory 112
 
0.5%
Other values (202) 3216
 
14.9%
2025-08-05T11:59:14.255029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 13636
12.0%
N 12384
10.9%
I 12306
10.9%
U 12230
10.8%
8456
 
7.5%
a 6522
 
5.8%
l 5668
 
5.0%
S 5028
 
4.4%
i 3720
 
3.3%
m 3572
 
3.2%
Other values (64) 29736
26.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113258
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 13636
12.0%
N 12384
10.9%
I 12306
10.9%
U 12230
10.8%
8456
 
7.5%
a 6522
 
5.8%
l 5668
 
5.0%
S 5028
 
4.4%
i 3720
 
3.3%
m 3572
 
3.2%
Other values (64) 29736
26.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113258
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 13636
12.0%
N 12384
10.9%
I 12306
10.9%
U 12230
10.8%
8456
 
7.5%
a 6522
 
5.8%
l 5668
 
5.0%
S 5028
 
4.4%
i 3720
 
3.3%
m 3572
 
3.2%
Other values (64) 29736
26.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113258
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 13636
12.0%
N 12384
10.9%
I 12306
10.9%
U 12230
10.8%
8456
 
7.5%
a 6522
 
5.8%
l 5668
 
5.0%
S 5028
 
4.4%
i 3720
 
3.3%
m 3572
 
3.2%
Other values (64) 29736
26.3%

yrcompiled
Real number (ℝ)

Distinct17
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014.188344
Minimum2008
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.6 KiB
2025-08-05T11:59:14.343568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2008
5-th percentile2008
Q12008
median2016
Q32019
95-th percentile2023
Maximum2024
Range16
Interquartile range (IQR)11

Descriptive statistics

Standard deviation5.565034446
Coefficient of variation (CV)0.002762916617
Kurtosis-1.485708719
Mean2014.188344
Median Absolute Deviation (MAD)5
Skewness0.08156168615
Sum26542974
Variance30.96960838
MonotonicityNot monotonic
2025-08-05T11:59:14.434010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2008 5296
40.2%
2016 1800
 
13.7%
2020 1086
 
8.2%
2017 978
 
7.4%
2019 882
 
6.7%
2021 710
 
5.4%
2024 604
 
4.6%
2015 472
 
3.6%
2018 424
 
3.2%
2022 356
 
2.7%
Other values (7) 570
 
4.3%
ValueCountFrequency (%)
2008 5296
40.2%
2009 16
 
0.1%
2010 12
 
0.1%
2011 34
 
0.3%
2012 252
 
1.9%
ValueCountFrequency (%)
2024 604
4.6%
2023 168
 
1.3%
2022 356
 
2.7%
2021 710
5.4%
2020 1086
8.2%
Distinct72
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size103.1 KiB
2025-08-05T11:59:14.647344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length168
Median length53
Mean length50.83502808
Min length3

Characters and Unicode

Total characters669904
Distinct characters74
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIUCN (International Union for Conservation of Nature)
2nd rowIUCN (International Union for Conservation of Nature)
3rd rowIUCN (International Union for Conservation of Nature)
4th rowIUCN (International Union for Conservation of Nature)
5th rowIUCN (International Union for Conservation of Nature)
ValueCountFrequency (%)
iucn 12600
13.9%
of 11318
12.5%
for 11274
12.5%
conservation 11268
12.5%
international 11262
12.5%
union 11262
12.5%
nature 11258
12.5%
group 1392
 
1.5%
ssc 1372
 
1.5%
specialist 1350
 
1.5%
Other values (184) 5980
6.6%
2025-08-05T11:59:15.001591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 80498
12.0%
77140
11.5%
o 69892
10.4%
a 51450
 
7.7%
r 47640
 
7.1%
t 47168
 
7.0%
i 37738
 
5.6%
e 37090
 
5.5%
C 25576
 
3.8%
N 23946
 
3.6%
Other values (64) 171766
25.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 669904
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 80498
12.0%
77140
11.5%
o 69892
10.4%
a 51450
 
7.7%
r 47640
 
7.1%
t 47168
 
7.0%
i 37738
 
5.6%
e 37090
 
5.5%
C 25576
 
3.8%
N 23946
 
3.6%
Other values (64) 171766
25.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 669904
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 80498
12.0%
77140
11.5%
o 69892
10.4%
a 51450
 
7.7%
r 47640
 
7.1%
t 47168
 
7.0%
i 37738
 
5.6%
e 37090
 
5.5%
C 25576
 
3.8%
N 23946
 
3.6%
Other values (64) 171766
25.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 669904
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 80498
12.0%
77140
11.5%
o 69892
10.4%
a 51450
 
7.7%
r 47640
 
7.1%
t 47168
 
7.0%
i 37738
 
5.6%
e 37090
 
5.5%
C 25576
 
3.8%
N 23946
 
3.6%
Other values (64) 171766
25.6%

subspecies
Text

Missing 

Distinct268
Distinct (%)20.2%
Missing11850
Missing (%)89.9%
Memory size103.1 KiB
2025-08-05T11:59:15.189790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length37
Median length21
Mean length8.310240964
Min length1

Characters and Unicode

Total characters11036
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd rowharrisoni
4th rowmatagalpae
5th rowapicalus
ValueCountFrequency (%)
manatus 106
 
7.7%
fascicularis 68
 
5.0%
milneedwarsii 46
 
3.4%
barbatus 44
 
3.2%
kulan 44
 
3.2%
0 38
 
2.8%
falconeri 36
 
2.6%
scitulus 30
 
2.2%
oi 26
 
1.9%
hemionus 22
 
1.6%
Other values (263) 908
66.4%
2025-08-05T11:59:15.500911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 1338
12.1%
a 1294
11.7%
s 1186
10.7%
r 784
 
7.1%
n 758
 
6.9%
u 752
 
6.8%
e 714
 
6.5%
t 576
 
5.2%
o 574
 
5.2%
l 540
 
4.9%
Other values (25) 2520
22.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11036
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 1338
12.1%
a 1294
11.7%
s 1186
10.7%
r 784
 
7.1%
n 758
 
6.9%
u 752
 
6.8%
e 714
 
6.5%
t 576
 
5.2%
o 574
 
5.2%
l 540
 
4.9%
Other values (25) 2520
22.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11036
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 1338
12.1%
a 1294
11.7%
s 1186
10.7%
r 784
 
7.1%
n 758
 
6.9%
u 752
 
6.8%
e 714
 
6.5%
t 576
 
5.2%
o 574
 
5.2%
l 540
 
4.9%
Other values (25) 2520
22.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11036
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 1338
12.1%
a 1294
11.7%
s 1186
10.7%
r 784
 
7.1%
n 758
 
6.9%
u 752
 
6.8%
e 714
 
6.5%
t 576
 
5.2%
o 574
 
5.2%
l 540
 
4.9%
Other values (25) 2520
22.8%

subpop
Text

Missing 

Distinct120
Distinct (%)48.4%
Missing12930
Missing (%)98.1%
Memory size103.1 KiB
2025-08-05T11:59:15.727794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length49
Median length30
Mean length16.96774194
Min length1

Characters and Unicode

Total characters4208
Distinct characters73
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd rowMediterranean
4th rowWrangel Island
5th rowSweden - Härjedalen
ValueCountFrequency (%)
nunavut 24
 
3.8%
island 22
 
3.5%
22
 
3.5%
east 12
 
1.9%
gobi 12
 
1.9%
0 10
 
1.6%
south 8
 
1.3%
peninsula 8
 
1.3%
area 8
 
1.3%
region 8
 
1.3%
Other values (200) 496
78.7%
2025-08-05T11:59:16.077640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 462
 
11.0%
382
 
9.1%
n 290
 
6.9%
e 268
 
6.4%
t 222
 
5.3%
r 202
 
4.8%
i 200
 
4.8%
l 186
 
4.4%
o 182
 
4.3%
s 176
 
4.2%
Other values (63) 1638
38.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4208
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 462
 
11.0%
382
 
9.1%
n 290
 
6.9%
e 268
 
6.4%
t 222
 
5.3%
r 202
 
4.8%
i 200
 
4.8%
l 186
 
4.4%
o 182
 
4.3%
s 176
 
4.2%
Other values (63) 1638
38.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4208
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 462
 
11.0%
382
 
9.1%
n 290
 
6.9%
e 268
 
6.4%
t 222
 
5.3%
r 202
 
4.8%
i 200
 
4.8%
l 186
 
4.4%
o 182
 
4.3%
s 176
 
4.2%
Other values (63) 1638
38.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4208
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 462
 
11.0%
382
 
9.1%
n 290
 
6.9%
e 268
 
6.4%
t 222
 
5.3%
r 202
 
4.8%
i 200
 
4.8%
l 186
 
4.4%
o 182
 
4.3%
s 176
 
4.2%
Other values (63) 1638
38.9%

source
Text

Missing 

Distinct331
Distinct (%)30.6%
Missing12098
Missing (%)91.8%
Memory size103.1 KiB
2025-08-05T11:59:16.362792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length254
Median length209
Mean length57.43888889
Min length1

Characters and Unicode

Total characters62034
Distinct characters96
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLunde et al. (2004), Bannikova et al. (2023)
2nd rowTom Gelatt
3rd rowAchmadi et al. 2014
4th rowsRedList platform
5th rowSovada et al. 2009
ValueCountFrequency (%)
et 556
 
6.1%
al 552
 
6.0%
and 312
 
3.4%
j 250
 
2.7%
2020 170
 
1.9%
of 126
 
1.4%
status 126
 
1.4%
recent 120
 
1.3%
c 120
 
1.3%
rowell 112
 
1.2%
Other values (1002) 6716
73.3%
2025-08-05T11:59:16.791822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8100
 
13.1%
a 4172
 
6.7%
e 3656
 
5.9%
t 2820
 
4.5%
n 2632
 
4.2%
. 2558
 
4.1%
i 2446
 
3.9%
r 2444
 
3.9%
o 2350
 
3.8%
0 2254
 
3.6%
Other values (86) 28602
46.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 62034
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8100
 
13.1%
a 4172
 
6.7%
e 3656
 
5.9%
t 2820
 
4.5%
n 2632
 
4.2%
. 2558
 
4.1%
i 2446
 
3.9%
r 2444
 
3.9%
o 2350
 
3.8%
0 2254
 
3.6%
Other values (86) 28602
46.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 62034
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8100
 
13.1%
a 4172
 
6.7%
e 3656
 
5.9%
t 2820
 
4.5%
n 2632
 
4.2%
. 2558
 
4.1%
i 2446
 
3.9%
r 2444
 
3.9%
o 2350
 
3.8%
0 2254
 
3.6%
Other values (86) 28602
46.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 62034
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8100
 
13.1%
a 4172
 
6.7%
e 3656
 
5.9%
t 2820
 
4.5%
n 2632
 
4.2%
. 2558
 
4.1%
i 2446
 
3.9%
r 2444
 
3.9%
o 2350
 
3.8%
0 2254
 
3.6%
Other values (86) 28602
46.1%

island
Text

Missing 

Distinct1016
Distinct (%)16.5%
Missing7002
Missing (%)53.1%
Memory size103.1 KiB
2025-08-05T11:59:17.056900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length44
Median length23
Mean length7.764248705
Min length1

Characters and Unicode

Total characters47952
Distinct characters70
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNosy Boraha
2nd rowLuzon
3rd rowMindanao
4th rowBohol
5th rowPanay
ValueCountFrequency (%)
new 312
 
4.1%
island 276
 
3.7%
guinea 196
 
2.6%
borneo 182
 
2.4%
sulawesi 164
 
2.2%
sumatra 156
 
2.1%
java 94
 
1.2%
islands 74
 
1.0%
madagascar 72
 
1.0%
luzon 66
 
0.9%
Other values (993) 5956
78.9%
2025-08-05T11:59:17.432044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 7494
15.6%
n 3732
 
7.8%
i 3340
 
7.0%
e 3048
 
6.4%
o 3020
 
6.3%
r 2680
 
5.6%
u 2368
 
4.9%
l 2070
 
4.3%
s 1840
 
3.8%
t 1440
 
3.0%
Other values (60) 16920
35.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47952
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 7494
15.6%
n 3732
 
7.8%
i 3340
 
7.0%
e 3048
 
6.4%
o 3020
 
6.3%
r 2680
 
5.6%
u 2368
 
4.9%
l 2070
 
4.3%
s 1840
 
3.8%
t 1440
 
3.0%
Other values (60) 16920
35.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47952
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 7494
15.6%
n 3732
 
7.8%
i 3340
 
7.0%
e 3048
 
6.4%
o 3020
 
6.3%
r 2680
 
5.6%
u 2368
 
4.9%
l 2070
 
4.3%
s 1840
 
3.8%
t 1440
 
3.0%
Other values (60) 16920
35.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47952
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 7494
15.6%
n 3732
 
7.8%
i 3340
 
7.0%
e 3048
 
6.4%
o 3020
 
6.3%
r 2680
 
5.6%
u 2368
 
4.9%
l 2070
 
4.3%
s 1840
 
3.8%
t 1440
 
3.0%
Other values (60) 16920
35.3%

tax_comm
Text

Missing 

Distinct36
Distinct (%)36.0%
Missing13078
Missing (%)99.2%
Memory size103.1 KiB
2025-08-05T11:59:17.607465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length147
Median length64
Mean length50.5
Min length17

Characters and Unicode

Total characters5050
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIncludes the form juliacae which is sometimes considered to be a distinct species.
2nd rowTwo eastern polygons may not be fraterculus.
3rd rowOryzomys meridensis is a synonym.
4th rowPrevioulsy listed as Triaenops furculus
5th rowPrevioulsy listed as Triaenops furculus
ValueCountFrequency (%)
a 36
 
4.6%
species 30
 
3.8%
the 30
 
3.8%
in 24
 
3.1%
is 24
 
3.1%
formerly 18
 
2.3%
be 18
 
2.3%
genus 18
 
2.3%
considered 14
 
1.8%
of 14
 
1.8%
Other values (133) 558
71.2%
2025-08-05T11:59:17.908547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
686
 
13.6%
e 478
 
9.5%
s 420
 
8.3%
i 328
 
6.5%
o 284
 
5.6%
r 274
 
5.4%
n 254
 
5.0%
a 218
 
4.3%
c 188
 
3.7%
l 186
 
3.7%
Other values (43) 1734
34.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5050
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
686
 
13.6%
e 478
 
9.5%
s 420
 
8.3%
i 328
 
6.5%
o 284
 
5.6%
r 274
 
5.4%
n 254
 
5.0%
a 218
 
4.3%
c 188
 
3.7%
l 186
 
3.7%
Other values (43) 1734
34.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5050
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
686
 
13.6%
e 478
 
9.5%
s 420
 
8.3%
i 328
 
6.5%
o 284
 
5.6%
r 274
 
5.4%
n 254
 
5.0%
a 218
 
4.3%
c 188
 
3.7%
l 186
 
3.7%
Other values (43) 1734
34.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5050
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
686
 
13.6%
e 478
 
9.5%
s 420
 
8.3%
i 328
 
6.5%
o 284
 
5.6%
r 274
 
5.4%
n 254
 
5.0%
a 218
 
4.3%
c 188
 
3.7%
l 186
 
3.7%
Other values (43) 1734
34.3%

dist_comm
Text

Missing 

Distinct169
Distinct (%)26.7%
Missing12546
Missing (%)95.2%
Memory size103.1 KiB
2025-08-05T11:59:18.074470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length224
Median length91
Mean length26.85443038
Min length1

Characters and Unicode

Total characters16972
Distinct characters74
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd rowMt. Giluwe
4th rowMt. Wilhelm
5th rowMt. Albert Edward
ValueCountFrequency (%)
distribution 194
 
8.3%
the 98
 
4.2%
known 96
 
4.1%
general 76
 
3.3%
for 48
 
2.1%
reintroduced 42
 
1.8%
assessment 40
 
1.7%
individuals 38
 
1.6%
single 36
 
1.5%
original 34
 
1.5%
Other values (384) 1632
69.9%
2025-08-05T11:59:18.372673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1700
 
10.0%
i 1504
 
8.9%
n 1326
 
7.8%
e 1318
 
7.8%
a 1132
 
6.7%
t 1098
 
6.5%
o 1062
 
6.3%
r 988
 
5.8%
s 930
 
5.5%
u 630
 
3.7%
Other values (64) 5284
31.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16972
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1700
 
10.0%
i 1504
 
8.9%
n 1326
 
7.8%
e 1318
 
7.8%
a 1132
 
6.7%
t 1098
 
6.5%
o 1062
 
6.3%
r 988
 
5.8%
s 930
 
5.5%
u 630
 
3.7%
Other values (64) 5284
31.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16972
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1700
 
10.0%
i 1504
 
8.9%
n 1326
 
7.8%
e 1318
 
7.8%
a 1132
 
6.7%
t 1098
 
6.5%
o 1062
 
6.3%
r 988
 
5.8%
s 930
 
5.5%
u 630
 
3.7%
Other values (64) 5284
31.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16972
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1700
 
10.0%
i 1504
 
8.9%
n 1326
 
7.8%
e 1318
 
7.8%
a 1132
 
6.7%
t 1098
 
6.5%
o 1062
 
6.3%
r 988
 
5.8%
s 930
 
5.5%
u 630
 
3.7%
Other values (64) 5284
31.1%

generalisd
Real number (ℝ)

Zeros 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.004856579147
Minimum0
Maximum1
Zeros13114
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size51.6 KiB
2025-08-05T11:59:18.450186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.06952236734
Coefficient of variation (CV)14.31508995
Kurtosis200.9878363
Mean0.004856579147
Median Absolute Deviation (MAD)0
Skewness14.24630944
Sum64
Variance0.004833359561
MonotonicityNot monotonic
2025-08-05T11:59:18.526281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 13114
99.5%
1 64
 
0.5%
ValueCountFrequency (%)
0 13114
99.5%
1 64
 
0.5%
ValueCountFrequency (%)
1 64
 
0.5%
0 13114
99.5%

legend
Text

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size103.1 KiB
2025-08-05T11:59:18.627777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length45
Median length17
Mean length17.64425558
Min length7

Characters and Unicode

Total characters232516
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowExtant (resident)
2nd rowExtant (resident)
3rd rowPossibly Extant (resident)
4th rowExtant (resident)
5th rowExtant (resident)
ValueCountFrequency (%)
extant 12630
45.9%
resident 12536
45.5%
possibly 446
 
1.6%
438
 
1.6%
extinct 368
 
1.3%
uncertain 310
 
1.1%
introduced 220
 
0.8%
presence 180
 
0.7%
reintroduced 130
 
0.5%
probably 92
 
0.3%
Other values (7) 186
 
0.7%
2025-08-05T11:59:18.846799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 39282
16.9%
n 26846
11.5%
e 26506
11.4%
14358
 
6.2%
i 14004
 
6.0%
s 13800
 
5.9%
r 13558
 
5.8%
a 13248
 
5.7%
d 13246
 
5.7%
x 12998
 
5.6%
Other values (20) 44670
19.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 232516
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 39282
16.9%
n 26846
11.5%
e 26506
11.4%
14358
 
6.2%
i 14004
 
6.0%
s 13800
 
5.9%
r 13558
 
5.8%
a 13248
 
5.7%
d 13246
 
5.7%
x 12998
 
5.6%
Other values (20) 44670
19.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 232516
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 39282
16.9%
n 26846
11.5%
e 26506
11.4%
14358
 
6.2%
i 14004
 
6.0%
s 13800
 
5.9%
r 13558
 
5.8%
a 13248
 
5.7%
d 13246
 
5.7%
x 12998
 
5.6%
Other values (20) 44670
19.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 232516
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 39282
16.9%
n 26846
11.5%
e 26506
11.4%
14358
 
6.2%
i 14004
 
6.0%
s 13800
 
5.9%
r 13558
 
5.8%
a 13248
 
5.7%
d 13246
 
5.7%
x 12998
 
5.6%
Other values (20) 44670
19.2%

kingdom
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size103.1 KiB
2025-08-05T11:59:18.919885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters105424
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowANIMALIA
2nd rowANIMALIA
3rd rowANIMALIA
4th rowANIMALIA
5th rowANIMALIA
ValueCountFrequency (%)
animalia 13178
100.0%
2025-08-05T11:59:19.076034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 39534
37.5%
I 26356
25.0%
N 13178
 
12.5%
M 13178
 
12.5%
L 13178
 
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 105424
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 39534
37.5%
I 26356
25.0%
N 13178
 
12.5%
M 13178
 
12.5%
L 13178
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 105424
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 39534
37.5%
I 26356
25.0%
N 13178
 
12.5%
M 13178
 
12.5%
L 13178
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 105424
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 39534
37.5%
I 26356
25.0%
N 13178
 
12.5%
M 13178
 
12.5%
L 13178
 
12.5%

phylum
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size103.1 KiB
2025-08-05T11:59:19.141270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters105424
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCHORDATA
2nd rowCHORDATA
3rd rowCHORDATA
4th rowCHORDATA
5th rowCHORDATA
ValueCountFrequency (%)
chordata 13178
100.0%
2025-08-05T11:59:19.301776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 26356
25.0%
H 13178
12.5%
C 13178
12.5%
O 13178
12.5%
R 13178
12.5%
D 13178
12.5%
T 13178
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 105424
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 26356
25.0%
H 13178
12.5%
C 13178
12.5%
O 13178
12.5%
R 13178
12.5%
D 13178
12.5%
T 13178
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 105424
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 26356
25.0%
H 13178
12.5%
C 13178
12.5%
O 13178
12.5%
R 13178
12.5%
D 13178
12.5%
T 13178
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 105424
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 26356
25.0%
H 13178
12.5%
C 13178
12.5%
O 13178
12.5%
R 13178
12.5%
D 13178
12.5%
T 13178
12.5%

class
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size103.1 KiB
2025-08-05T11:59:19.367495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters105424
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMAMMALIA
2nd rowMAMMALIA
3rd rowMAMMALIA
4th rowMAMMALIA
5th rowMAMMALIA
ValueCountFrequency (%)
mammalia 13178
100.0%
2025-08-05T11:59:19.524760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M 39534
37.5%
A 39534
37.5%
L 13178
 
12.5%
I 13178
 
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 105424
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 39534
37.5%
A 39534
37.5%
L 13178
 
12.5%
I 13178
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 105424
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 39534
37.5%
A 39534
37.5%
L 13178
 
12.5%
I 13178
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 105424
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 39534
37.5%
A 39534
37.5%
L 13178
 
12.5%
I 13178
 
12.5%

order_
Text

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size103.1 KiB
2025-08-05T11:59:19.625279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length16
Median length15
Mean length9.89224465
Min length6

Characters and Unicode

Total characters130360
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPRIMATES
2nd rowPILOSA
3rd rowPILOSA
4th rowCHIROPTERA
5th rowCHIROPTERA
ValueCountFrequency (%)
chiroptera 4786
36.3%
rodentia 2864
21.7%
artiodactyla 1276
 
9.7%
carnivora 988
 
7.5%
primates 970
 
7.4%
eulipotyphla 672
 
5.1%
diprotodontia 518
 
3.9%
dasyuromorphia 150
 
1.1%
didelphimorphia 144
 
1.1%
sirenia 136
 
1.0%
Other values (15) 674
 
5.1%
2025-08-05T11:59:19.836817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 18420
14.1%
A 17408
13.4%
I 13996
10.7%
O 13432
10.3%
T 13288
10.2%
E 10122
7.8%
P 8550
6.6%
C 7430
5.7%
H 6140
 
4.7%
D 6020
 
4.6%
Other values (10) 15554
11.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 130360
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 18420
14.1%
A 17408
13.4%
I 13996
10.7%
O 13432
10.3%
T 13288
10.2%
E 10122
7.8%
P 8550
6.6%
C 7430
5.7%
H 6140
 
4.7%
D 6020
 
4.6%
Other values (10) 15554
11.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 130360
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 18420
14.1%
A 17408
13.4%
I 13996
10.7%
O 13432
10.3%
T 13288
10.2%
E 10122
7.8%
P 8550
6.6%
C 7430
5.7%
H 6140
 
4.7%
D 6020
 
4.6%
Other values (10) 15554
11.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 130360
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 18420
14.1%
A 17408
13.4%
I 13996
10.7%
O 13432
10.3%
T 13288
10.2%
E 10122
7.8%
P 8550
6.6%
C 7430
5.7%
H 6140
 
4.7%
D 6020
 
4.6%
Other values (10) 15554
11.9%

family
Text

Distinct137
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size103.1 KiB
2025-08-05T11:59:20.001523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length15
Mean length10.94217635
Min length6

Characters and Unicode

Total characters144196
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCHEIROGALEIDAE
2nd rowCYCLOPEDIDAE
3rd rowCYCLOPEDIDAE
4th rowHIPPOSIDERIDAE
5th rowHIPPOSIDERIDAE
ValueCountFrequency (%)
pteropodidae 1732
 
13.1%
vespertilionidae 1206
 
9.2%
muridae 1128
 
8.6%
cricetidae 702
 
5.3%
bovidae 694
 
5.3%
soricidae 568
 
4.3%
hipposideridae 444
 
3.4%
cercopithecidae 442
 
3.4%
rhinolophidae 392
 
3.0%
sciuridae 386
 
2.9%
Other values (127) 5484
41.6%
2025-08-05T11:59:20.283789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 22760
15.8%
I 20754
14.4%
D 16362
11.3%
A 15338
10.6%
O 11136
7.7%
R 9908
6.9%
P 8466
 
5.9%
T 6180
 
4.3%
C 5760
 
4.0%
L 4760
 
3.3%
Other values (12) 22772
15.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 144196
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 22760
15.8%
I 20754
14.4%
D 16362
11.3%
A 15338
10.6%
O 11136
7.7%
R 9908
6.9%
P 8466
 
5.9%
T 6180
 
4.3%
C 5760
 
4.0%
L 4760
 
3.3%
Other values (12) 22772
15.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 144196
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 22760
15.8%
I 20754
14.4%
D 16362
11.3%
A 15338
10.6%
O 11136
7.7%
R 9908
6.9%
P 8466
 
5.9%
T 6180
 
4.3%
C 5760
 
4.0%
L 4760
 
3.3%
Other values (12) 22772
15.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 144196
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 22760
15.8%
I 20754
14.4%
D 16362
11.3%
A 15338
10.6%
O 11136
7.7%
R 9908
6.9%
P 8466
 
5.9%
T 6180
 
4.3%
C 5760
 
4.0%
L 4760
 
3.3%
Other values (12) 22772
15.8%

genus
Text

Distinct924
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size103.1 KiB
2025-08-05T11:59:20.512768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length17
Median length14
Mean length8.91379572
Min length3

Characters and Unicode

Total characters117466
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMicrocebus
2nd rowCyclopes
3rd rowCyclopes
4th rowHipposideros
5th rowHipposideros
ValueCountFrequency (%)
pteropus 402
 
3.1%
rhinolophus 392
 
3.0%
hipposideros 376
 
2.9%
macroglossus 338
 
2.6%
myotis 332
 
2.5%
crocidura 288
 
2.2%
dobsonia 272
 
2.1%
nyctimene 178
 
1.4%
pipistrellus 174
 
1.3%
macaca 168
 
1.3%
Other values (914) 10258
77.8%
2025-08-05T11:59:20.849118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 12848
 
10.9%
o 12088
 
10.3%
r 8122
 
6.9%
a 7946
 
6.8%
u 7870
 
6.7%
i 7866
 
6.7%
e 7428
 
6.3%
l 5426
 
4.6%
t 5346
 
4.6%
c 4938
 
4.2%
Other values (40) 37588
32.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 117466
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 12848
 
10.9%
o 12088
 
10.3%
r 8122
 
6.9%
a 7946
 
6.8%
u 7870
 
6.7%
i 7866
 
6.7%
e 7428
 
6.3%
l 5426
 
4.6%
t 5346
 
4.6%
c 4938
 
4.2%
Other values (40) 37588
32.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 117466
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 12848
 
10.9%
o 12088
 
10.3%
r 8122
 
6.9%
a 7946
 
6.8%
u 7870
 
6.7%
i 7866
 
6.7%
e 7428
 
6.3%
l 5426
 
4.6%
t 5346
 
4.6%
c 4938
 
4.2%
Other values (40) 37588
32.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 117466
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 12848
 
10.9%
o 12088
 
10.3%
r 8122
 
6.9%
a 7946
 
6.8%
u 7870
 
6.7%
i 7866
 
6.7%
e 7428
 
6.3%
l 5426
 
4.6%
t 5346
 
4.6%
c 4938
 
4.2%
Other values (40) 37588
32.0%
Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size103.1 KiB
2025-08-05T11:59:20.927438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters26356
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDD
2nd rowLC
3rd rowLC
4th rowLC
5th rowLC
ValueCountFrequency (%)
lc 8008
60.8%
vu 1644
 
12.5%
en 1048
 
8.0%
dd 1016
 
7.7%
nt 1008
 
7.6%
cr 444
 
3.4%
ex 10
 
0.1%
2025-08-05T11:59:21.081012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 8452
32.1%
L 8008
30.4%
N 2056
 
7.8%
D 2032
 
7.7%
U 1644
 
6.2%
V 1644
 
6.2%
E 1058
 
4.0%
T 1008
 
3.8%
R 444
 
1.7%
X 10
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26356
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 8452
32.1%
L 8008
30.4%
N 2056
 
7.8%
D 2032
 
7.7%
U 1644
 
6.2%
V 1644
 
6.2%
E 1058
 
4.0%
T 1008
 
3.8%
R 444
 
1.7%
X 10
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26356
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 8452
32.1%
L 8008
30.4%
N 2056
 
7.8%
D 2032
 
7.7%
U 1644
 
6.2%
V 1644
 
6.2%
E 1058
 
4.0%
T 1008
 
3.8%
R 444
 
1.7%
X 10
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26356
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 8452
32.1%
L 8008
30.4%
N 2056
 
7.8%
D 2032
 
7.7%
U 1644
 
6.2%
V 1644
 
6.2%
E 1058
 
4.0%
T 1008
 
3.8%
R 444
 
1.7%
X 10
 
< 0.1%

marine
Text

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size103.1 KiB
2025-08-05T11:59:21.146375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.970860525
Min length4

Characters and Unicode

Total characters65506
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfalse
2nd rowfalse
3rd rowfalse
4th rowfalse
5th rowfalse
ValueCountFrequency (%)
false 12794
97.1%
true 384
 
2.9%
2025-08-05T11:59:21.304853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 13178
20.1%
f 12794
19.5%
l 12794
19.5%
a 12794
19.5%
s 12794
19.5%
t 384
 
0.6%
r 384
 
0.6%
u 384
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 65506
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 13178
20.1%
f 12794
19.5%
l 12794
19.5%
a 12794
19.5%
s 12794
19.5%
t 384
 
0.6%
r 384
 
0.6%
u 384
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 65506
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 13178
20.1%
f 12794
19.5%
l 12794
19.5%
a 12794
19.5%
s 12794
19.5%
t 384
 
0.6%
r 384
 
0.6%
u 384
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 65506
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 13178
20.1%
f 12794
19.5%
l 12794
19.5%
a 12794
19.5%
s 12794
19.5%
t 384
 
0.6%
r 384
 
0.6%
u 384
 
0.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size103.1 KiB
2025-08-05T11:59:21.363409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.018971012
Min length4

Characters and Unicode

Total characters52962
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtrue
2nd rowtrue
3rd rowtrue
4th rowtrue
5th rowtrue
ValueCountFrequency (%)
true 12928
98.1%
false 250
 
1.9%
2025-08-05T11:59:21.517843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 13178
24.9%
t 12928
24.4%
r 12928
24.4%
u 12928
24.4%
f 250
 
0.5%
a 250
 
0.5%
l 250
 
0.5%
s 250
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 52962
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 13178
24.9%
t 12928
24.4%
r 12928
24.4%
u 12928
24.4%
f 250
 
0.5%
a 250
 
0.5%
l 250
 
0.5%
s 250
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 52962
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 13178
24.9%
t 12928
24.4%
r 12928
24.4%
u 12928
24.4%
f 250
 
0.5%
a 250
 
0.5%
l 250
 
0.5%
s 250
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 52962
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 13178
24.9%
t 12928
24.4%
r 12928
24.4%
u 12928
24.4%
f 250
 
0.5%
a 250
 
0.5%
l 250
 
0.5%
s 250
 
0.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size103.1 KiB
2025-08-05T11:59:21.580474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.960843831
Min length4

Characters and Unicode

Total characters65374
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfalse
2nd rowfalse
3rd rowfalse
4th rowfalse
5th rowfalse
ValueCountFrequency (%)
false 12662
96.1%
true 516
 
3.9%
2025-08-05T11:59:21.737054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 13178
20.2%
f 12662
19.4%
l 12662
19.4%
a 12662
19.4%
s 12662
19.4%
t 516
 
0.8%
r 516
 
0.8%
u 516
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 65374
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 13178
20.2%
f 12662
19.4%
l 12662
19.4%
a 12662
19.4%
s 12662
19.4%
t 516
 
0.8%
r 516
 
0.8%
u 516
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 65374
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 13178
20.2%
f 12662
19.4%
l 12662
19.4%
a 12662
19.4%
s 12662
19.4%
t 516
 
0.8%
r 516
 
0.8%
u 516
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 65374
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 13178
20.2%
f 12662
19.4%
l 12662
19.4%
a 12662
19.4%
s 12662
19.4%
t 516
 
0.8%
r 516
 
0.8%
u 516
 
0.8%

SHAPE_Leng
Real number (ℝ)

Distinct6126
Distinct (%)46.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.95347145
Minimum0.0004386663515
Maximum11628.5903
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size103.1 KiB
2025-08-05T11:59:21.832558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0004386663515
5-th percentile0.196485918
Q11.432590442
median6.86059135
Q337.93617456
95-th percentile250.5712985
Maximum11628.5903
Range11628.58986
Interquartile range (IQR)36.50358412

Descriptive statistics

Standard deviation417.3211874
Coefficient of variation (CV)5.567736615
Kurtosis339.8903021
Mean74.95347145
Median Absolute Deviation (MAD)6.472415342
Skewness16.29387174
Sum987736.8468
Variance174156.9734
MonotonicityNot monotonic
2025-08-05T11:59:21.956660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.906926095 30
 
0.2%
42.44858319 16
 
0.1%
4.103421855 16
 
0.1%
57.76234096 16
 
0.1%
0.9030914056 14
 
0.1%
25.22829762 14
 
0.1%
5.567196789 12
 
0.1%
4.216660671 10
 
0.1%
5.507212154 10
 
0.1%
2.034867632 10
 
0.1%
Other values (6116) 13030
98.9%
ValueCountFrequency (%)
0.0004386663515 2
< 0.1%
0.000792709655 2
< 0.1%
0.01167093147 2
< 0.1%
0.01188659221 2
< 0.1%
0.0183596676 2
< 0.1%
ValueCountFrequency (%)
11628.5903 2
< 0.1%
11199.45058 2
< 0.1%
11001.24969 2
< 0.1%
8234.266307 2
< 0.1%
8142.254115 2
< 0.1%

SHAPE_Area
Real number (ℝ)

Skewed 

Distinct6130
Distinct (%)46.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean130.631878
Minimum1.531225851 × 10-8
Maximum36689.66836
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size103.1 KiB
2025-08-05T11:59:22.081154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.531225851 × 10-8
5-th percentile0.00162524246
Q10.0509449452
median0.9248023858
Q316.45733698
95-th percentile355.8609696
Maximum36689.66836
Range36689.66836
Interquartile range (IQR)16.40639204

Descriptive statistics

Standard deviation1208.632992
Coefficient of variation (CV)9.252205591
Kurtosis508.1211976
Mean130.631878
Median Absolute Deviation (MAD)0.9224858458
Skewness20.68505755
Sum1721466.888
Variance1460793.709
MonotonicityNot monotonic
2025-08-05T11:59:22.207805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4008237602 30
 
0.2%
13.77260719 16
 
0.1%
0.1835855243 16
 
0.1%
9.302263315 16
 
0.1%
0.02579748003 14
 
0.1%
6.356186735 14
 
0.1%
0.3664353151 12
 
0.1%
0.1094121799 10
 
0.1%
0.02647178502 10
 
0.1%
0.5743873419 10
 
0.1%
Other values (6120) 13030
98.9%
ValueCountFrequency (%)
1.531225851 × 10-82
< 0.1%
5.000430248 × 10-82
< 0.1%
2.550214666 × 10-62
< 0.1%
5.240337402 × 10-62
< 0.1%
5.358066924 × 10-62
< 0.1%
ValueCountFrequency (%)
36689.66836 2
< 0.1%
36616.54468 2
< 0.1%
36542.19535 2
< 0.1%
26964.93241 2
< 0.1%
22421.10391 2
< 0.1%